We test this code on NVIDIA A100 and report the throughput in the followings.
settings
throughput (imgs/s)
baseline
928
+channels_last
992
+amp
1459
+channels_last&&
2260
Check for NVIDIA impl and Throughput Benchmark at https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/Classification/ConvNets/resnet50v1.5/README.md#training-performance-results
Test environment:
PyTorch version: 1.12.1+cu113
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A
OS: Ubuntu 20.04.4 LTS (x86_64)
GCC version: Could not collect
Clang version: Could not collect
CMake version: Could not collect
Libc version: glibc-2.31
Python version: 3.10.6 | packaged by conda-forge | (main, Aug 22 2022, 20:35:26) [GCC 10.4.0] (64-bit runtime)
Python platform: Linux-4.15.0-192-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: Could not collect
GPU models and configuration: GPU 0: NVIDIA A100-SXM4-40GB
Nvidia driver version: 470.129.06
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
Versions of relevant libraries:
[pip3] numpy==1.23.3
[pip3] pytorch-lightning==1.7.6
[pip3] torch==1.12.1+cu113
[pip3] torchaudio==0.12.1+cu113
[pip3] torchmetrics==0.9.3
[pip3] torchvision==0.13.1+cu113
[conda] numpy 1.23.3 py310h53a5b5f_0 conda-forge
[conda] pytorch-lightning 1.7.6 pypi_0 pypi
[conda] torch 1.12.1+cu113 pypi_0 pypi
[conda] torchaudio 0.12.1+cu113 pypi_0 pypi
[conda] torchmetrics 0.9.3 pypi_0 pypi
[conda] torchvision 0.13.1+cu113 pypi_0 pypi
More Benchmarks on PyTorch 2.0 We test this code on NVIDIA V100 and report the throughput in the followings.
settings
throughput (imgs/s)
baseline
345
+channels_last
345
+amp
774
+channels_last&&
1175
+channels_last&&&&compile(default)
1228
+channels_last&&&&compile(default+fullgraph)
1228
+channels_last&&&&compile(reduce-overhead)
1234
+channels_last&&&&compile(max-autotune)
FAIL
Benchmarks on MobileNetV2
settings
throughput (imgs/s)
baseline
813
+channels_last
420
+amp
1315
+channels_last&&
2100
+channels_last&&&&compile(default)
2316
Benchmarks on ShuffleNetV2
settings
throughput (imgs/s)
baseline
2342
+channels_last
1854
+amp
3250
+channels_last&&
3862
+channels_last&&&&compile(default)
4711
settings
throughput (imgs/s)
baseline
102
+amp
360
+amp&&compile(default)
289
Benchmarks on SwinTransformer-tiny
settings
throughput (imgs/s)
baseline
264
+amp
499
+amp&&compile(default)
789
Test environment:
PyTorch version: 2.0.0+cu118
Is debug build: False
CUDA used to build PyTorch: 11.8
ROCM used to build PyTorch: N/A
OS: Ubuntu 22.04.1 LTS (x86_64)
GCC version: (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
Clang version: Could not collect
CMake version: version 3.25.0
Libc version: glibc-2.35
Python version: 3.10.9 | packaged by conda-forge | (main, Feb 2 2023, 20:20:04) [GCC 11.3.0] (64-bit runtime)
Python platform: Linux-5.4.0-139-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 11.8.89
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: Tesla V100-SXM2-32GB
Nvidia driver version: 525.85.12
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.7.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.7.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True
CPU:
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 10
On-line CPU(s) list: 0-9
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Platinum 8255C CPU @ 2.50GHz
CPU family: 6
Model: 85
Thread(s) per core: 1
Core(s) per socket: 10
Socket(s): 1
Stepping: 5
BogoMIPS: 4999.99
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single pti fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm mpx avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 arat avx512_vnni
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 320 KiB (10 instances)
L1i cache: 320 KiB (10 instances)
L2 cache: 40 MiB (10 instances)
L3 cache: 35.8 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-9
Vulnerability Itlb multihit: KVM: Vulnerable
Vulnerability L1tf: Mitigation; PTE Inversion
Vulnerability Mds: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Vulnerability Retbleed: Vulnerable
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines, STIBP disabled, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown
Versions of relevant libraries:
[pip3] numpy==1.23.5
[pip3] torch==2.0.0+cu118
[pip3] torchaudio==2.0.1+cu118
[pip3] torchdata==0.6.0
[pip3] torchvision==0.15.1+cu118
[conda] numpy 1.23.5 pypi_0 pypi
[conda] torch 2.0.0+cu118 pypi_0 pypi
[conda] torchaudio 2.0.1+cu118 pypi_0 pypi
[conda] torchdata 0.6.0 pypi_0 pypi
[conda] torchvision 0.15.1+cu118 pypi_0 pypi